NDLI: Ear verification under uncontrolled conditions with convolutional neural networks

Content Provider	IET Digital Library
Author	Zhang, Yi Mu, Zhichun Yuan, Li Yu, Chen
Abstract	The capabilities of biometric systems have recently made extraordinary leaps by the emergence of deep learning. However, due to the lack of enough training data, the applications of the deep neural network in the ear recognition filed have run into the bottleneck. Moreover, the effect of fine-tuning from some pre-trained models is far less than expected due to the diversity among different tasks. Therefore, the authors propose a large-scale ear database and explore the robust convolutional neural network (CNN) architecture for the ear feature representation. The images in this USTB-Helloear database were taken under uncontrolled conditions with illumination, pose variation and different level of ear occlusions. Then they fine-tuned and modified some deep models on the proposed database through the ear verification experiments. First, they replaced the last pooling layers by spatial pyramid pooling layers to fit arbitrary data size and obtain multi-level features. In the training phase, the CNNs were trained both under the supervision of the softmax loss and centre loss to obtain more compact and discriminative features to identify unseen ears. Finally, three CNNs with different scales of ear images were assembled as the multi-scale ear representations for ear verification. The experimental results demonstrate the effectiveness of the proposed modified CNN deep model.
Starting Page	185
Ending Page	198
Page Count	14
ISSN	20474938
Volume Number	7
e-ISSN	20474946
Issue Number	Issue 3, May (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/7/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2017.0176
Journal	IET Biometrics
Publisher Date	2018-01-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Arbitrary Data Size Biometric System Biomimetics CNN Deep Model Computer Vision And Image Processing Technique Convolution Convolutional Neural Network Architecture Data Security Deep Learning Deep Neural Network Discriminative Features Ear Ear Feature Representation Ear Occlusions Ear Recognition Ear Verification Feature Extraction Feedforward Neural Network Image Recognition Image Representation Large-scale Ear Database Learning in AI Multilevel Feature Multiscale Ear Representations Neural Computing Technique Softmax Loss Spatial Pyramid Pooling Layer Unseen Ears USTB-Helloear Database
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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